feat(train): hook in max_seq_length to set custom context length; max 131K, default 2K - #22

Text SFT trained at a fixed max_seq_length of 2048 and silently truncated
longer rows. The length is now derived per run from the dataset: the
longest row, rounded up to 1024, capped at 32768. Short datasets keep
their throughput (the calibration probe measures at the configured
length, so a fixed 32k would have collapsed every run to micro-batch 1);
long ones get what they need. Nothing is exposed to the user: no tool
argument, no mention of sequence or context length in the normal flow.

- defaults: MAX_SEQ_LENGTH_CEILING, derived_seq_length(), auto_seq_length
  flag on HParams; DPO/GRPO/vision keep the fixed 2048.
- seq_length (new): submit-time estimate with the base model's
  tokenizer.json via the `tokenizers` wheel (new core dep), character
  fallback when offline (non-ASCII counted one-to-one); streams parquet.
  The backend planner sizes the GPU from the shipped value.
- sft: exact re-measure through the real chat template before the probe,
  capped at the model's max_position_embeddings; rows over the limit are
  dropped with a count (never truncated); counts persisted; all-eval-
  dropped warned. Probe "no_fit" now fails fast with a plain message
  instead of training on the default micro-batch; a skipped probe at
  long context falls back to micro-batch 1.
- calibrate: maybe_autotune_batch_size returns cached|probed|no_fit|skipped.
- Hidden expert override LQH_MAX_SEQ_LENGTH pins the value.
- Skill text, failure messages and the VLM collator error no longer name
  the knob. Unit conftest stubs the Hub tokenizer fetch.

Supersedes PR #22; PR #23 (assistant_only_loss) rebases onto this and
shares tokenized_row_lengths.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_015JpgUssR166axwm95srXbg